Abstract
Hydrogel has a complex network structure with inhomogeneous and random distribution of polymer chains. Much effort has been paid to fully understand the relationship between mesoscopic network structure and macroscopic mechanical properties of hydrogels. In this paper, we develop a deep learning approach to predict the mechanical properties of hydrogels from polymer network structures. First, network structural models of hydrogels are constructed from mesoscopic scale using self-avoiding walk method. The constructed model is similar to the real hydrogel network. Then, two deep learning models are proposed to capture the nonlinear mapping from mesoscopic hydrogel network structural model to its macroscale mechanical property. A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels under uniaxial tension. Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates that the deep learning models are able to capture the internal relationship between complex network structures and mechanical properties. We hope this approach can provide guidance to structural design and material property design of different soft materials.
Highlights
With remarkable mechanical properties, hydrogels demonstrate high potential to be one of the advanced smart materials in the future [1,2]
On the basic paradigm of Deep learning (DL) algorithms presented in previous section, we propose two architectures of the DL models, one is the deep neural network (DNN) model based on multilayer perceptron (MLP), the other is the 3D convolutional neural networks (CNNs) model
It is worth mentioning that batch normalization is a technique for training deep neural networks that standardizes the inputs to a layer for each mini-batch [54]
Summary
Hydrogels demonstrate high potential to be one of the advanced smart materials in the future [1,2]. Because the effect of polymer network structure on the mechanical properties of hydrogel is significant, a deeper understanding of polymer network can help us to better utilize the existed material and create new material. It is imperative to investigate the relationship between the network structures and mechanical properties of hydrogels. The mechanical properties of hydrogels are studied from different scales, from microscopic scale to continuum scale. It is because that the continuum scale model cannot reflect the real structure of hydrogel network. The mechanical properties of hydrogels are usually studied using molecular dynamics methods. The mesoscopic hydrogel model is expected to link the microscopic and the continuum scales and act as an important complement between them, providing a new theoretical framework for hydrogel research. Obtaining valuable information from the mesoscale model can help to better understand the relationship between network structure and mechanical property. We develop a generating method of mesoscopic hydrogel network based on self-avoiding walk (SAW)
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